Correlation between aggregated molecular cancer subtypes and selected clinical features

Glioma (Primary solid tumor)

28 January 2016 | analyses__2016_01_28

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1251HKM

Overview

Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 14 different clustering approaches and 9 clinical features across 1109 patients, 60 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL_TYPE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', and 'HISTOLOGICAL_TYPE'.
CNMF clustering analysis on array-based miR expression data identified 4 subtypes that correlate to 'Time to Death'.
Consensus hierarchical clustering analysis on array-based miR expression data identified 3 subtypes that do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', 'RACE', and 'ETHNICITY'.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', and 'RACE'.
CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'GENDER', and 'HISTOLOGICAL_TYPE'.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', and 'ETHNICITY'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death', 'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.
6 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', and 'RACE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 14 different clustering approaches and 9 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 60 significant findings detected.


Clinical Features	Time to Death	YEARS TO BIRTH	TUMOR TISSUE SITE	GENDER	RADIATION THERAPY	KARNOFSKY PERFORMANCE SCORE	HISTOLOGICAL TYPE	RACE	ETHNICITY
Statistical Tests	logrank test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Fisher's exact test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Fisher's exact test
mRNA CNMF subtypes	0.0968 (0.179)	0.123 (0.215)		0.112 (0.202)	0.411 (0.528)	0.606 (0.706)	0.00232 (0.00635)	0.943 (1.00)	0.165 (0.27)
mRNA cHierClus subtypes	0.000163 (0.000514)	0.00252 (0.00675)		0.093 (0.176)	0.227 (0.34)	0.515 (0.631)	0.0301 (0.0642)	0.586 (0.69)	0.684 (0.776)
miR CNMF subtypes	0.00783 (0.0194)	0.71 (0.799)		0.508 (0.628)	0.291 (0.399)	0.839 (0.928)	0.548 (0.658)	0.133 (0.229)	1 (1.00)
miR cHierClus subtypes	0.245 (0.355)	0.12 (0.212)		0.183 (0.292)	0.478 (0.596)	0.261 (0.365)	0.544 (0.658)	0.193 (0.303)	0.677 (0.775)
Copy Number Ratio CNMF subtypes	0 (0)	9.69e-72 (2.03e-70)	1e-05 (3.71e-05)	0.0755 (0.151)	1e-05 (3.71e-05)	2.32e-19 (2.92e-18)	1e-05 (3.71e-05)	0.0221 (0.0526)	0.00057 (0.00167)
METHLYATION CNMF	0 (0)	3.26e-45 (5.88e-44)	1e-05 (3.71e-05)	0.994 (1.00)	1e-05 (3.71e-05)	2.52e-11 (2.11e-10)	1e-05 (3.71e-05)	0.00012 (0.000398)	0.144 (0.246)
RPPA CNMF subtypes	0.155 (0.257)	0.0277 (0.0602)	0.00043 (0.00129)	0.00294 (0.00772)	0.0938 (0.176)	0.195 (0.303)	7e-05 (0.000238)	0.0772 (0.152)	0.154 (0.257)
RPPA cHierClus subtypes	0.0256 (0.0571)	0.00215 (0.00602)	0.0007 (0.002)	0.202 (0.31)	0.0124 (0.03)	0.0704 (0.143)	2e-05 (7e-05)	0.36 (0.475)	0.943 (1.00)
RNAseq CNMF subtypes	0 (0)	1.83e-31 (2.57e-30)	1e-05 (3.71e-05)	0.444 (0.565)	1e-05 (3.71e-05)	1.38e-16 (1.59e-15)	1e-05 (3.71e-05)	0.384 (0.498)	0.0258 (0.0571)
RNAseq cHierClus subtypes	0 (0)	4.19e-44 (6.6e-43)	1e-05 (3.71e-05)	0.362 (0.475)	1e-05 (3.71e-05)	4.73e-15 (4.97e-14)	1e-05 (3.71e-05)	0.171 (0.276)	0.239 (0.351)
MIRSEQ CNMF	0.0253 (0.0571)	0.804 (0.896)		0.618 (0.714)	0.00617 (0.0155)	0.0898 (0.174)	1e-05 (3.71e-05)	0.3 (0.407)	0.258 (0.365)
MIRSEQ CHIERARCHICAL	3.47e-14 (3.37e-13)	1.78e-08 (1.4e-07)		0.285 (0.394)	1e-05 (3.71e-05)	0.000189 (0.000582)	1e-05 (3.71e-05)	0.567 (0.674)	0.234 (0.347)
MIRseq Mature CNMF subtypes	0.000159 (0.000514)	0.112 (0.202)		0.469 (0.591)	2e-05 (7e-05)	0.0239 (0.0558)	1e-05 (3.71e-05)	0.347 (0.466)	0.0654 (0.135)
MIRseq Mature cHierClus subtypes	0 (0)	1.88e-13 (1.7e-12)		0.217 (0.329)	1e-05 (3.71e-05)	0.00414 (0.0106)	1e-05 (3.71e-05)	0.0461 (0.0967)	0.258 (0.365)

Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1. Description of clustering approach #1: 'mRNA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	167	97	139	122

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0968 (logrank test), Q value = 0.18

Table S2. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	523	448	0.1 - 127.6 (12.4)
subtype1	167	149	0.1 - 127.6 (11.3)
subtype2	97	83	0.2 - 115.9 (13.6)
subtype3	138	114	0.1 - 94.8 (13.8)
subtype4	121	102	0.2 - 91.8 (12.7)

Figure S1. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.123 (Kruskal-Wallis (anova)), Q value = 0.22

Table S3. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	525	57.7 (14.6)
subtype1	167	58.4 (12.1)
subtype2	97	54.3 (17.3)
subtype3	139	60.0 (13.8)
subtype4	122	56.7 (15.7)

Figure S2. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.112 (Fisher's exact test), Q value = 0.2

Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	205	320
subtype1	63	104
subtype2	44	53
subtype3	60	79
subtype4	38	84

Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'mRNA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.411 (Fisher's exact test), Q value = 0.53

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	70	435
subtype1	21	138
subtype2	16	77
subtype3	14	121
subtype4	19	99

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'mRNA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.606 (Kruskal-Wallis (anova)), Q value = 0.71

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	396	77.1 (14.6)
subtype1	125	77.6 (15.4)
subtype2	75	76.3 (11.4)
subtype3	107	76.4 (15.7)
subtype4	89	78.2 (14.6)

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00232 (Fisher's exact test), Q value = 0.0064

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	6	20	499
subtype1	1	8	158
subtype2	0	8	89
subtype3	0	3	136
subtype4	5	1	116

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.943 (Fisher's exact test), Q value = 1

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	31	462
subtype1	4	12	146
subtype2	2	7	84
subtype3	4	7	125
subtype4	3	5	107

Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'RACE'

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.165 (Fisher's exact test), Q value = 0.27

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	438
subtype1	3	139
subtype2	5	72
subtype3	3	121
subtype4	1	106

Figure S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S10. Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	153	107	103	162

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.000163 (logrank test), Q value = 0.00051

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	523	448	0.1 - 127.6 (12.4)
subtype1	153	138	0.1 - 91.0 (12.2)
subtype2	107	81	0.2 - 115.9 (14.9)
subtype3	102	86	0.1 - 94.8 (13.8)
subtype4	161	143	0.1 - 127.6 (10.6)

Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00252 (Kruskal-Wallis (anova)), Q value = 0.0068

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	525	57.7 (14.6)
subtype1	153	56.9 (13.6)
subtype2	107	52.9 (17.9)
subtype3	103	60.6 (12.1)
subtype4	162	59.8 (13.7)

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.093 (Fisher's exact test), Q value = 0.18

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	205	320
subtype1	51	102
subtype2	44	63
subtype3	50	53
subtype4	60	102

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.227 (Fisher's exact test), Q value = 0.34

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	70	435
subtype1	17	131
subtype2	13	91
subtype3	11	88
subtype4	29	125

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'mRNA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.515 (Kruskal-Wallis (anova)), Q value = 0.63

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	396	77.1 (14.6)
subtype1	118	78.1 (15.4)
subtype2	84	77.6 (11.6)
subtype3	81	75.2 (15.6)
subtype4	113	77.3 (15.1)

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0301 (Fisher's exact test), Q value = 0.064

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	6	20	499
subtype1	0	11	142
subtype2	1	3	103
subtype3	0	3	100
subtype4	5	3	154

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.586 (Fisher's exact test), Q value = 0.69

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	31	462
subtype1	4	13	130
subtype2	4	6	95
subtype3	3	4	93
subtype4	2	8	144

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'RACE'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.684 (Fisher's exact test), Q value = 0.78

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	438
subtype1	5	124
subtype2	1	91
subtype3	2	90
subtype4	4	133

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #3: 'miR CNMF subtypes'

Table S19. Description of clustering approach #3: 'miR CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	151	189	93	130

'miR CNMF subtypes' versus 'Time to Death'

P value = 0.00783 (logrank test), Q value = 0.019

Table S20. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	561	469	0.1 - 127.6 (12.2)
subtype1	151	130	0.1 - 91.0 (12.1)
subtype2	189	155	0.1 - 127.6 (13.0)
subtype3	92	78	0.4 - 65.3 (9.6)
subtype4	129	106	0.1 - 120.6 (12.5)

Figure S17. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'miR CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.71 (Kruskal-Wallis (anova)), Q value = 0.8

Table S21. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	563	57.9 (14.3)
subtype1	151	59.7 (11.8)
subtype2	189	56.5 (16.0)
subtype3	93	57.8 (15.3)
subtype4	130	58.1 (13.7)

Figure S18. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'miR CNMF subtypes' versus 'GENDER'

P value = 0.508 (Fisher's exact test), Q value = 0.63

Table S22. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	219	344
subtype1	59	92
subtype2	80	109
subtype3	36	57
subtype4	44	86

Figure S19. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'miR CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.291 (Fisher's exact test), Q value = 0.4

Table S23. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	73	466
subtype1	17	129
subtype2	22	157
subtype3	18	72
subtype4	16	108

Figure S20. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'miR CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.839 (Kruskal-Wallis (anova)), Q value = 0.93

Table S24. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	430	77.0 (15.6)
subtype1	119	76.0 (16.1)
subtype2	138	76.4 (17.2)
subtype3	75	78.0 (13.5)
subtype4	98	78.4 (14.3)

Figure S21. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'miR CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.548 (Fisher's exact test), Q value = 0.66

Table S25. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	11	20	532
subtype1	2	3	146
subtype2	3	9	177
subtype3	3	5	85
subtype4	3	3	124

Figure S22. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'miR CNMF subtypes' versus 'RACE'

P value = 0.133 (Fisher's exact test), Q value = 0.23

Table S26. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #8: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	30	495
subtype1	3	13	127
subtype2	7	9	162
subtype3	1	1	89
subtype4	2	7	117

Figure S23. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #8: 'RACE'

'miR CNMF subtypes' versus 'ETHNICITY'

P value = 1 (Fisher's exact test), Q value = 1

Table S27. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	461
subtype1	3	124
subtype2	4	158
subtype3	2	74
subtype4	3	105

Figure S24. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #4: 'miR cHierClus subtypes'

Table S28. Description of clustering approach #4: 'miR cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	303	129	131

'miR cHierClus subtypes' versus 'Time to Death'

P value = 0.245 (logrank test), Q value = 0.35

Table S29. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	561	469	0.1 - 127.6 (12.2)
subtype1	302	255	0.1 - 120.6 (12.7)
subtype2	128	106	0.1 - 92.7 (11.0)
subtype3	131	108	0.1 - 127.6 (11.8)

Figure S25. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'miR cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.12 (Kruskal-Wallis (anova)), Q value = 0.21

Table S30. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	563	57.9 (14.3)
subtype1	303	56.6 (15.9)
subtype2	129	59.0 (11.5)
subtype3	131	60.1 (12.5)

Figure S26. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'miR cHierClus subtypes' versus 'GENDER'

P value = 0.183 (Fisher's exact test), Q value = 0.29

Table S31. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	219	344
subtype1	111	192
subtype2	48	81
subtype3	60	71

Figure S27. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'miR cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.478 (Fisher's exact test), Q value = 0.6

Table S32. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	73	466
subtype1	44	246
subtype2	13	109
subtype3	16	111

Figure S28. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'miR cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.261 (Kruskal-Wallis (anova)), Q value = 0.37

Table S33. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	430	77.0 (15.6)
subtype1	228	78.2 (14.9)
subtype2	101	76.5 (15.6)
subtype3	101	74.8 (17.1)

Figure S29. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'miR cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.544 (Fisher's exact test), Q value = 0.66

Table S34. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	11	20	532
subtype1	7	13	283
subtype2	3	2	124
subtype3	1	5	125

Figure S30. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'miR cHierClus subtypes' versus 'RACE'

P value = 0.193 (Fisher's exact test), Q value = 0.3

Table S35. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #8: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	30	495
subtype1	7	10	273
subtype2	3	11	113
subtype3	3	9	109

Figure S31. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #8: 'RACE'

'miR cHierClus subtypes' versus 'ETHNICITY'

P value = 0.677 (Fisher's exact test), Q value = 0.78

Table S36. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	461
subtype1	6	250
subtype2	2	109
subtype3	4	102

Figure S32. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #5: 'Copy Number Ratio CNMF subtypes'

Table S37. Description of clustering approach #5: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	528	468	89

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0 (logrank test), Q value = 0

Table S38. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	1078	598	0.0 - 211.2 (16.0)
subtype1	526	433	0.1 - 211.2 (12.2)
subtype2	464	114	0.0 - 182.3 (24.0)
subtype3	88	51	0.1 - 117.5 (15.4)

Figure S33. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 9.69e-72 (Kruskal-Wallis (anova)), Q value = 2e-70

Table S39. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	1084	50.8 (15.8)
subtype1	528	59.2 (12.5)
subtype2	467	41.3 (13.4)
subtype3	89	51.0 (16.4)

Figure S34. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S40. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	BRAIN	CENTRAL NERVOUS SYSTEM
ALL	573	512
subtype1	461	67
subtype2	57	411
subtype3	55	34

Figure S35. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.0755 (Fisher's exact test), Q value = 0.15

Table S41. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	453	632
subtype1	207	321
subtype2	200	268
subtype3	46	43

Figure S36. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S42. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	258	770
subtype1	69	433
subtype2	170	271
subtype3	19	66

Figure S37. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 2.32e-19 (Kruskal-Wallis (anova)), Q value = 2.9e-18

Table S43. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	732	81.1 (15.3)
subtype1	383	76.7 (16.5)
subtype2	292	86.8 (12.0)
subtype3	57	81.2 (12.1)

Figure S38. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S44. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	GLIOBLASTOMA MULTIFORME (GBM)	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	194	30	129	189	18	525
subtype1	39	19	14	14	12	430
subtype2	137	6	104	170	4	47
subtype3	18	5	11	5	2	48

Figure S39. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.0221 (Fisher's exact test), Q value = 0.053

Table S45. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	21	71	959
subtype1	1	8	45	452
subtype2	0	11	18	428
subtype3	0	2	8	79

Figure S40. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.00057 (Fisher's exact test), Q value = 0.0017

Table S46. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	44	921
subtype1	9	443
subtype2	29	405
subtype3	6	73

Figure S41. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #6: 'METHLYATION CNMF'

Table S47. Description of clustering approach #6: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	179	225	88	161

'METHLYATION CNMF' versus 'Time to Death'

P value = 0 (logrank test), Q value = 0

Table S48. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	648	218	0.0 - 211.2 (18.8)
subtype1	178	120	0.2 - 211.2 (11.8)
subtype2	224	48	0.0 - 172.8 (26.2)
subtype3	88	28	0.1 - 146.1 (19.0)
subtype4	158	22	0.1 - 182.3 (23.2)

Figure S42. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 3.26e-45 (Kruskal-Wallis (anova)), Q value = 5.9e-44

Table S49. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	652	46.5 (14.9)
subtype1	179	59.4 (11.7)
subtype2	225	37.9 (10.9)
subtype3	88	44.4 (15.7)
subtype4	160	45.4 (12.6)

Figure S43. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S50. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	BRAIN	CENTRAL NERVOUS SYSTEM
ALL	138	515
subtype1	118	61
subtype2	8	217
subtype3	12	76
subtype4	0	161

Figure S44. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.994 (Fisher's exact test), Q value = 1

Table S51. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	288	365
subtype1	78	101
subtype2	99	126
subtype3	40	48
subtype4	71	90

Figure S45. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S52. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	204	404
subtype1	24	138
subtype2	61	152
subtype3	31	50
subtype4	88	64

Figure S46. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 2.52e-11 (Kruskal-Wallis (anova)), Q value = 2.1e-10

Table S53. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	404	84.1 (15.0)
subtype1	120	76.2 (18.0)
subtype2	144	87.6 (11.9)
subtype3	46	84.8 (12.8)
subtype4	94	88.5 (11.9)

Figure S47. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S54. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	GLIOBLASTOMA MULTIFORME (GBM)	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	194	21	130	191	1	116
subtype1	38	16	10	13	1	101
subtype2	119	3	66	32	0	5
subtype3	31	2	19	26	0	10
subtype4	6	0	35	120	0	0

Figure S48. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RACE'

P value = 0.00012 (Fisher's exact test), Q value = 4e-04

Table S55. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #8: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	45	582
subtype1	0	1	25	146
subtype2	0	2	8	214
subtype3	1	1	7	76
subtype4	0	4	5	146

Figure S49. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #8: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.144 (Fisher's exact test), Q value = 0.25

Table S56. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	33	544
subtype1	3	134
subtype2	14	196
subtype3	4	75
subtype4	12	139

Figure S50. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #7: 'RPPA CNMF subtypes'

Table S57. Description of clustering approach #7: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	219	171	161	109

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.155 (logrank test), Q value = 0.26

Table S58. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	654	269	0.0 - 211.2 (16.9)
subtype1	219	92	0.0 - 211.2 (17.1)
subtype2	168	56	0.1 - 154.4 (15.9)
subtype3	161	80	0.1 - 156.2 (16.8)
subtype4	106	41	0.1 - 130.8 (19.1)

Figure S51. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0277 (Kruskal-Wallis (anova)), Q value = 0.06

Table S59. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	659	48.6 (15.9)
subtype1	219	49.5 (15.2)
subtype2	171	46.8 (15.8)
subtype3	161	51.0 (16.3)
subtype4	108	46.3 (16.1)

Figure S52. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.00043 (Fisher's exact test), Q value = 0.0013

Table S60. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	BRAIN	CENTRAL NERVOUS SYSTEM
ALL	232	428
subtype1	90	129
subtype2	44	127
subtype3	69	92
subtype4	29	80

Figure S53. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.00294 (Fisher's exact test), Q value = 0.0077

Table S61. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	283	377
subtype1	77	142
subtype2	80	91
subtype3	85	76
subtype4	41	68

Figure S54. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0938 (Fisher's exact test), Q value = 0.18

Table S62. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	181	431
subtype1	52	150
subtype2	57	106
subtype3	38	112
subtype4	34	63

Figure S55. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.195 (Kruskal-Wallis (anova)), Q value = 0.3

Table S63. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	417	81.0 (16.0)
subtype1	136	80.7 (17.1)
subtype2	107	82.4 (15.3)
subtype3	100	78.3 (17.1)
subtype4	74	83.1 (12.9)

Figure S56. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 7e-05 (Fisher's exact test), Q value = 0.00024

Table S64. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	GLIOBLASTOMA MULTIFORME (GBM)	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	147	19	114	167	3	210
subtype1	44	6	37	48	2	82
subtype2	32	3	35	60	0	41
subtype3	36	8	15	41	1	60
subtype4	35	2	27	18	0	27

Figure S57. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.0772 (Fisher's exact test), Q value = 0.15

Table S65. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	45	578
subtype1	0	2	18	193
subtype2	1	3	8	156
subtype3	0	7	14	133
subtype4	0	0	5	96

Figure S58. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.154 (Fisher's exact test), Q value = 0.26

Table S66. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	29	573
subtype1	11	190
subtype2	7	154
subtype3	3	140
subtype4	8	89

Figure S59. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #8: 'RPPA cHierClus subtypes'

Table S67. Description of clustering approach #8: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	241	256	163

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0256 (logrank test), Q value = 0.057

Table S68. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	654	269	0.0 - 211.2 (16.9)
subtype1	240	120	0.1 - 211.2 (16.2)
subtype2	255	98	0.0 - 182.3 (17.2)
subtype3	159	51	0.1 - 154.4 (16.5)

Figure S60. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00215 (Kruskal-Wallis (anova)), Q value = 0.006

Table S69. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	659	48.6 (15.9)
subtype1	241	50.1 (16.0)
subtype2	255	49.7 (15.7)
subtype3	163	44.8 (15.3)

Figure S61. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 7e-04 (Fisher's exact test), Q value = 0.002

Table S70. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	BRAIN	CENTRAL NERVOUS SYSTEM
ALL	232	428
subtype1	90	151
subtype2	104	152
subtype3	38	125

Figure S62. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.202 (Fisher's exact test), Q value = 0.31

Table S71. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	283	377
subtype1	105	136
subtype2	100	156
subtype3	78	85

Figure S63. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0124 (Fisher's exact test), Q value = 0.03

Table S72. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	181	431
subtype1	56	166
subtype2	66	173
subtype3	59	92

Figure S64. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0704 (Kruskal-Wallis (anova)), Q value = 0.14

Table S73. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	417	81.0 (16.0)
subtype1	148	78.1 (19.0)
subtype2	171	81.9 (13.8)
subtype3	98	83.7 (14.0)

Figure S65. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 2e-05 (Fisher's exact test), Q value = 7e-05

Table S74. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	GLIOBLASTOMA MULTIFORME (GBM)	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	147	19	114	167	3	210
subtype1	61	8	33	57	2	80
subtype2	61	10	43	48	1	93
subtype3	25	1	38	62	0	37

Figure S66. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.36 (Fisher's exact test), Q value = 0.48

Table S75. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	45	578
subtype1	0	4	19	208
subtype2	0	5	20	222
subtype3	1	3	6	148

Figure S67. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.943 (Fisher's exact test), Q value = 1

Table S76. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	29	573
subtype1	10	210
subtype2	11	220
subtype3	8	143

Figure S68. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #9: 'RNAseq CNMF subtypes'

Table S77. Description of clustering approach #9: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	250	190	227

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0 (logrank test), Q value = 0

Table S78. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	662	245	0.0 - 211.2 (18.6)
subtype1	248	166	0.1 - 211.2 (13.3)
subtype2	188	40	0.1 - 172.8 (22.5)
subtype3	226	39	0.0 - 182.3 (26.4)

Figure S69. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.83e-31 (Kruskal-Wallis (anova)), Q value = 2.6e-30

Table S79. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	666	46.8 (15.1)
subtype1	250	55.7 (14.5)
subtype2	189	43.5 (14.1)
subtype3	227	39.7 (11.5)

Figure S70. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S80. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	BRAIN	CENTRAL NERVOUS SYSTEM
ALL	152	515
subtype1	149	101
subtype2	3	187
subtype3	0	227

Figure S71. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.444 (Fisher's exact test), Q value = 0.56

Table S81. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	283	384
subtype1	102	148
subtype2	88	102
subtype3	93	134

Figure S72. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S82. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	209	417
subtype1	38	195
subtype2	77	100
subtype3	94	122

Figure S73. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 1.38e-16 (Kruskal-Wallis (anova)), Q value = 1.6e-15

Table S83. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	424	83.6 (14.1)
subtype1	180	77.4 (14.8)
subtype2	106	86.1 (12.5)
subtype3	138	89.9 (10.5)

Figure S74. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S84. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	GLIOBLASTOMA MULTIFORME (GBM)	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	194	1	130	191	1	150
subtype1	72	1	15	14	1	147
subtype2	46	0	45	96	0	3
subtype3	76	0	70	81	0	0

Figure S75. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.384 (Fisher's exact test), Q value = 0.5

Table S85. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	13	31	611
subtype1	1	7	16	224
subtype2	0	3	8	174
subtype3	0	3	7	213

Figure S76. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.0258 (Fisher's exact test), Q value = 0.057

Table S86. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	35	574
subtype1	6	215
subtype2	11	168
subtype3	18	191

Figure S77. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #10: 'RNAseq cHierClus subtypes'

Table S87. Description of clustering approach #10: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	208	190	269

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0 (logrank test), Q value = 0

Table S88. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	662	245	0.0 - 211.2 (18.6)
subtype1	207	153	0.1 - 133.7 (11.7)
subtype2	189	42	0.0 - 211.2 (26.3)
subtype3	266	50	0.1 - 182.3 (22.9)

Figure S78. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 4.19e-44 (Kruskal-Wallis (anova)), Q value = 6.6e-43

Table S89. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	666	46.8 (15.1)
subtype1	208	58.7 (12.9)
subtype2	190	38.3 (11.1)
subtype3	268	43.5 (13.5)

Figure S79. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S90. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	BRAIN	CENTRAL NERVOUS SYSTEM
ALL	152	515
subtype1	139	69
subtype2	9	181
subtype3	4	265

Figure S80. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.362 (Fisher's exact test), Q value = 0.48

Table S91. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	283	384
subtype1	84	124
subtype2	76	114
subtype3	123	146

Figure S81. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S92. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	209	417
subtype1	34	158
subtype2	49	130
subtype3	126	129

Figure S82. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 4.73e-15 (Kruskal-Wallis (anova)), Q value = 5e-14

Table S93. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	424	83.6 (14.1)
subtype1	147	76.2 (15.1)
subtype2	125	87.8 (11.6)
subtype3	152	87.4 (11.9)

Figure S83. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S94. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	GLIOBLASTOMA MULTIFORME (GBM)	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	194	1	130	191	1	150
subtype1	44	0	13	12	1	138
subtype2	100	1	52	29	0	8
subtype3	50	0	65	150	0	4

Figure S84. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.171 (Fisher's exact test), Q value = 0.28

Table S95. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	13	31	611
subtype1	1	4	16	185
subtype2	0	3	7	178
subtype3	0	6	8	248

Figure S85. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.239 (Fisher's exact test), Q value = 0.35

Table S96. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	35	574
subtype1	6	174
subtype2	12	165
subtype3	17	235

Figure S86. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #11: 'MIRSEQ CNMF'

Table S97. Description of clustering approach #11: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4
Number of samples	140	106	83	182

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0253 (logrank test), Q value = 0.057

Table S98. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	507	124	0.0 - 211.2 (22.4)
subtype1	139	32	0.0 - 145.1 (23.5)
subtype2	106	38	0.1 - 211.2 (21.1)
subtype3	81	18	0.1 - 182.3 (24.9)
subtype4	181	36	0.1 - 156.2 (21.4)

Figure S87. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.804 (Kruskal-Wallis (anova)), Q value = 0.9

Table S99. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	510	43.0 (13.4)
subtype1	140	42.5 (13.4)
subtype2	106	42.4 (13.2)
subtype3	83	44.1 (13.2)
subtype4	181	43.1 (13.7)

Figure S88. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.618 (Fisher's exact test), Q value = 0.71

Table S100. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	230	281
subtype1	61	79
subtype2	43	63
subtype3	38	45
subtype4	88	94

Figure S89. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.00617 (Fisher's exact test), Q value = 0.016

Table S101. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	184	294
subtype1	45	84
subtype2	27	74
subtype3	39	39
subtype4	73	97

Figure S90. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0898 (Kruskal-Wallis (anova)), Q value = 0.17

Table S102. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	305	86.7 (12.6)
subtype1	86	86.7 (12.6)
subtype2	69	84.2 (12.9)
subtype3	47	89.4 (11.9)
subtype4	103	87.0 (12.7)

Figure S91. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S103. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	193	127	191
subtype1	69	35	36
subtype2	66	22	18
subtype3	5	20	58
subtype4	53	50	79

Figure S92. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.3 (Fisher's exact test), Q value = 0.41

Table S104. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	21	471
subtype1	1	1	10	125
subtype2	0	2	3	100
subtype3	0	3	2	75
subtype4	0	2	6	171

Figure S93. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.258 (Fisher's exact test), Q value = 0.37

Table S105. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	32	445
subtype1	9	121
subtype2	3	94
subtype3	8	67
subtype4	12	163

Figure S94. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #12: 'MIRSEQ CHIERARCHICAL'

Table S106. Description of clustering approach #12: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	222	186	103

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 3.47e-14 (logrank test), Q value = 3.4e-13

Table S107. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	507	124	0.0 - 211.2 (22.4)
subtype1	219	42	0.0 - 182.3 (27.4)
subtype2	185	36	0.1 - 172.8 (23.3)
subtype3	103	46	0.1 - 211.2 (17.5)

Figure S95. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 1.78e-08 (Kruskal-Wallis (anova)), Q value = 1.4e-07

Table S108. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	510	43.0 (13.4)
subtype1	222	40.0 (11.5)
subtype2	185	42.6 (13.8)
subtype3	103	50.0 (13.8)

Figure S96. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.285 (Fisher's exact test), Q value = 0.39

Table S109. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	230	281
subtype1	91	131
subtype2	90	96
subtype3	49	54

Figure S97. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S110. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	184	294
subtype1	91	119
subtype2	76	97
subtype3	17	78

Figure S98. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.000189 (Kruskal-Wallis (anova)), Q value = 0.00058

Table S111. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	305	86.7 (12.6)
subtype1	139	88.9 (11.1)
subtype2	105	87.0 (12.5)
subtype3	61	80.8 (14.3)

Figure S99. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S112. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	193	127	191
subtype1	76	67	79
subtype2	48	44	94
subtype3	69	16	18

Figure S100. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.567 (Fisher's exact test), Q value = 0.67

Table S113. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	21	471
subtype1	0	3	7	208
subtype2	0	3	8	170
subtype3	1	2	6	93

Figure S101. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.234 (Fisher's exact test), Q value = 0.35

Table S114. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	32	445
subtype1	17	188
subtype2	12	164
subtype3	3	93

Figure S102. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #13: 'MIRseq Mature CNMF subtypes'

Table S115. Description of clustering approach #13: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	126	119	106	156

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.000159 (logrank test), Q value = 0.00051

Table S116. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	503	124	0.0 - 211.2 (22.4)
subtype1	126	36	0.0 - 182.3 (21.2)
subtype2	118	44	0.1 - 145.1 (21.9)
subtype3	104	12	0.1 - 169.8 (25.4)
subtype4	155	32	0.1 - 211.2 (21.3)

Figure S103. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.112 (Kruskal-Wallis (anova)), Q value = 0.2

Table S117. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	506	43.0 (13.4)
subtype1	126	41.0 (14.1)
subtype2	119	44.5 (12.9)
subtype3	106	43.0 (12.0)
subtype4	155	43.5 (14.0)

Figure S104. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.469 (Fisher's exact test), Q value = 0.59

Table S118. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	229	278
subtype1	53	73
subtype2	49	70
subtype3	50	56
subtype4	77	79

Figure S105. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 2e-05 (Fisher's exact test), Q value = 7e-05

Table S119. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	183	292
subtype1	38	81
subtype2	27	81
subtype3	57	44
subtype4	61	86

Figure S106. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0239 (Kruskal-Wallis (anova)), Q value = 0.056

Table S120. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	304	86.7 (12.6)
subtype1	66	86.7 (12.4)
subtype2	83	84.3 (13.4)
subtype3	68	90.1 (11.0)
subtype4	87	86.3 (12.8)

Figure S107. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S121. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	191	126	190
subtype1	58	36	32
subtype2	74	23	22
subtype3	13	26	67
subtype4	46	41	69

Figure S108. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.347 (Fisher's exact test), Q value = 0.47

Table S122. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	21	467
subtype1	0	0	5	119
subtype2	1	2	7	107
subtype3	0	4	3	95
subtype4	0	2	6	146

Figure S109. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.0654 (Fisher's exact test), Q value = 0.14

Table S123. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	32	441
subtype1	5	114
subtype2	6	102
subtype3	13	86
subtype4	8	139

Figure S110. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #14: 'MIRseq Mature cHierClus subtypes'

Table S124. Description of clustering approach #14: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	98	77	120	57	63	92

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0 (logrank test), Q value = 0

Table S125. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	503	124	0.0 - 211.2 (22.4)
subtype1	97	13	0.0 - 172.8 (27.4)
subtype2	76	21	0.1 - 134.3 (26.0)
subtype3	119	24	0.1 - 169.8 (21.6)
subtype4	56	7	0.1 - 123.7 (24.7)
subtype5	63	37	0.1 - 133.7 (15.7)
subtype6	92	22	0.1 - 211.2 (22.4)

Figure S111. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.88e-13 (Kruskal-Wallis (anova)), Q value = 1.7e-12

Table S126. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	506	43.0 (13.4)
subtype1	98	36.6 (11.2)
subtype2	77	41.4 (11.5)
subtype3	119	43.0 (13.9)
subtype4	57	44.1 (12.6)
subtype5	63	54.6 (11.7)
subtype6	92	42.6 (13.1)

Figure S112. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.217 (Fisher's exact test), Q value = 0.33

Table S127. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	229	278
subtype1	34	64
subtype2	36	41
subtype3	59	61
subtype4	23	34
subtype5	31	32
subtype6	46	46

Figure S113. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S128. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	183	292
subtype1	34	58
subtype2	19	55
subtype3	47	66
subtype4	37	17
subtype5	11	45
subtype6	35	51

Figure S114. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.00414 (Kruskal-Wallis (anova)), Q value = 0.011

Table S129. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	304	86.7 (12.6)
subtype1	60	87.3 (11.8)
subtype2	53	86.8 (11.2)
subtype3	65	85.7 (12.5)
subtype4	36	91.4 (9.9)
subtype5	37	79.7 (15.9)
subtype6	53	88.9 (12.4)

Figure S115. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S130. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	191	126	190
subtype1	55	27	16
subtype2	35	20	22
subtype3	35	28	57
subtype4	2	11	44
subtype5	41	11	11
subtype6	23	29	40

Figure S116. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.0461 (Fisher's exact test), Q value = 0.097

Table S131. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	21	467
subtype1	0	0	2	94
subtype2	0	2	3	72
subtype3	0	2	5	112
subtype4	0	3	2	50
subtype5	1	1	7	53
subtype6	0	0	2	86

Figure S117. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.258 (Fisher's exact test), Q value = 0.37

Table S132. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	32	441
subtype1	6	81
subtype2	4	69
subtype3	5	110
subtype4	8	44
subtype5	3	55
subtype6	6	82

Figure S118. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/GBMLGG-TP/22573865/GBMLGG-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/GBMLGG-TP/22506567/GBMLGG-TP.merged_data.txt
Number of patients = 1109
Number of clustering approaches = 14
Number of selected clinical features = 9
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References

[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)

[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)

[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[4] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)

[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

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